IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v180y2017i1p119-139.html
   My bibliography  Save this article

A space–time multivariate Bayesian model to analyse road traffic accidents by severity

Author

Listed:
  • Areti Boulieri
  • Silvia Liverani
  • Kees Hoogh
  • Marta Blangiardo

Abstract

No abstract is available for this item.

Suggested Citation

  • Areti Boulieri & Silvia Liverani & Kees Hoogh & Marta Blangiardo, 2017. "A space–time multivariate Bayesian model to analyse road traffic accidents by severity," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(1), pages 119-139, January.
    • Handle: RePEc:bla:jorssa:v:180:y:2017:i:1:p:119-139
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/rssa.12178
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Mardia, K. V., 1988. "Multi-dimensional multivariate Gaussian Markov random fields with application to image processing," Journal of Multivariate Analysis, Elsevier, vol. 24(2), pages 265-284, February.
    2. Julian Besag & Jeremy York & Annie Mollié, 1991. "Bayesian image restoration, with two applications in spatial statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 43(1), pages 1-20, March.
    3. Durbin, James & Koopman, Siem Jan, 2012. "Time Series Analysis by State Space Methods," OUP Catalogue, Oxford University Press, edition 2, number 9780199641178, Decembrie.
    4. Tom Brijs & Dimitris Karlis & Filip Van den Bossche & Geert Wets, 2007. "A Bayesian model for ranking hazardous road sites," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(4), pages 1001-1017, October.
    5. Daniel J. Graham & Stephen Glaister, 2003. "Spatial Variation in Road Pedestrian Casualties: The Role of Urban Scale, Density and Land-use Mix," Urban Studies, Urban Studies Journal Limited, vol. 40(8), pages 1591-1607, July.
    6. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    7. Frits Bijleveld & Jacques Commandeur & Siem Jan Koopman & Kees van Montfort, 2010. "Multivariate non‐linear time series modelling of exposure and risk in road safety research," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(1), pages 145-161, January.
    8. Song, J.J. & Ghosh, M. & Miaou, S. & Mallick, B., 2006. "Bayesian multivariate spatial models for roadway traffic crash mapping," Journal of Multivariate Analysis, Elsevier, vol. 97(1), pages 246-273, January.
    9. Ying C. MacNab & C. B. Dean, 2001. "Autoregressive Spatial Smoothing and Temporal Spline Smoothing for Mapping Rates," Biometrics, The International Biometric Society, vol. 57(3), pages 949-956, September.
    10. Ludwig Fahrmeir & Stefan Lang, 2001. "Bayesian inference for generalized additive mixed models based on Markov random field priors," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 50(2), pages 201-220.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Andrea Gilardi & Jorge Mateu & Riccardo Borgoni & Robin Lovelace, 2022. "Multivariate hierarchical analysis of car crashes data considering a spatial network lattice," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(3), pages 1150-1177, July.
    2. Briz-Redón, Álvaro & Iftimi, Adina & Montes, Francisco, 2022. "Accounting for previous events to model and predict traffic accidents at the road segment level: A study in Valencia (Spain)," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    3. Bhowmik, Tanmoy & Yasmin, Shamsunnahar & Eluru, Naveen, 2021. "A New Econometric Approach for Modeling Several Count Variables: A Case Study of Crash Frequency Analysis by Crash Type and Severity," Transportation Research Part B: Methodological, Elsevier, vol. 153(C), pages 172-203.
    4. Beecham, Roger & Lovelace, Robin, 2022. "A framework for inserting visually-supported inferences into geographical analysis workflow: application to road safety research," OSF Preprints mfja8, Center for Open Science.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gamerman, Dani & Moreira, Ajax R. B., 2004. "Multivariate spatial regression models," Journal of Multivariate Analysis, Elsevier, vol. 91(2), pages 262-281, November.
    2. Volker Schmid & Leonhard Held, 2004. "Bayesian Extrapolation of Space–Time Trends in Cancer Registry Data," Biometrics, The International Biometric Society, vol. 60(4), pages 1034-1042, December.
    3. Klein, Nadja & Herwartz, Helmut & Kneib, Thomas, 2020. "Modelling regional patterns of inefficiency: A Bayesian approach to geoadditive panel stochastic frontier analysis with an application to cereal production in England and Wales," Journal of Econometrics, Elsevier, vol. 214(2), pages 513-539.
    4. Wu, Peijie & Meng, Xianghai & Song, Li, 2021. "Bayesian space–time modeling of bicycle and pedestrian crash risk by injury severity levels to explore the long-term spatiotemporal effects," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    5. Craig Anderson & Louise M. Ryan, 2017. "A Comparison of Spatio-Temporal Disease Mapping Approaches Including an Application to Ischaemic Heart Disease in New South Wales, Australia," IJERPH, MDPI, vol. 14(2), pages 1-16, February.
    6. Håvard Rue & Ingelin Steinsland & Sveinung Erland, 2004. "Approximating hidden Gaussian Markov random fields," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(4), pages 877-892, November.
    7. Tamvada, Jagannadha Pawan, 2010. "The Dynamics of Self-employment in a Developing Country: Evidence from India," MPRA Paper 20042, University Library of Munich, Germany.
    8. Congdon, Peter, 2006. "A model for non-parametric spatially varying regression effects," Computational Statistics & Data Analysis, Elsevier, vol. 50(2), pages 422-445, January.
    9. Peter Congdon, 2010. "A Multilevel Model for Comorbid Outcomes: Obesity and Diabetes in the US," IJERPH, MDPI, vol. 7(2), pages 1-20, January.
    10. Xiaoping Jin & Bradley P. Carlin & Sudipto Banerjee, 2005. "Generalized Hierarchical Multivariate CAR Models for Areal Data," Biometrics, The International Biometric Society, vol. 61(4), pages 950-961, December.
    11. Peter Congdon, 2014. "Estimating life expectancies for US small areas: a regression framework," Journal of Geographical Systems, Springer, vol. 16(1), pages 1-18, January.
    12. Xiaoping Jin & Sudipto Banerjee & Bradley P. Carlin, 2007. "Order‐free co‐regionalized areal data models with application to multiple‐disease mapping," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(5), pages 817-838, November.
    13. Wheeler, David C. & Hickson, DeMarc A. & Waller, Lance A., 2010. "Assessing local model adequacy in Bayesian hierarchical models using the partitioned deviance information criterion," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1657-1671, June.
    14. Ying C. MacNab, 2003. "Hierarchical Bayesian Modeling of Spatially Correlated Health Service Outcome and Utilization Rates," Biometrics, The International Biometric Society, vol. 59(2), pages 305-315, June.
    15. Daniel J. Graham & Emma J. McCoy & David A. Stephens, 2013. "Quantifying the effect of area deprivation on child pedestrian casualties by using longitudinal mixed models to adjust for confounding, interference and spatial dependence," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 176(4), pages 931-950, October.
    16. Leonhard Knorr-Held & Günter Raßer & Nikolaus Becker, 2002. "Disease Mapping of Stage-Specific Cancer Incidence Data," Biometrics, The International Biometric Society, vol. 58(3), pages 492-501, September.
    17. Nushrat Nazia & Zahid Ahmad Butt & Melanie Lyn Bedard & Wang-Choi Tang & Hibah Sehar & Jane Law, 2022. "Methods Used in the Spatial and Spatiotemporal Analysis of COVID-19 Epidemiology: A Systematic Review," IJERPH, MDPI, vol. 19(14), pages 1-28, July.
    18. Alexander Razen & Stefan Lang & Judith Santer, 2016. "Estimation of Spatially Correlated Random Scaling Factors based on Markov Random Field Priors," Working Papers 2016-33, Faculty of Economics and Statistics, Universität Innsbruck.
    19. Brezger, Andreas & Lang, Stefan, 2006. "Generalized structured additive regression based on Bayesian P-splines," Computational Statistics & Data Analysis, Elsevier, vol. 50(4), pages 967-991, February.
    20. Eibich, Peter & Ziebarth, Nicolas, 2014. "Examining the Structure of Spatial Health Effects in Germany Using Hierarchical Bayes Models," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 49, pages 305-320.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:jorssa:v:180:y:2017:i:1:p:119-139. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.